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Fault Diagnosis of Bearings and Gears Based on LiteNet With Feature Aggregation

https://doi.org/10.1109/tim.2023.3259032

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Vibration-based fault diagnosis of the natural gas compressor using adaptive stochastic resonance realized by Generative Adversarial Networks

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Conditional empirical wavelet transform with modified ratio of cyclic content for bearing fault diagnosis

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Dive Into Deep Learning

inferredcites →https://doi.org/10.1016/j.measurement.2020.107660openalex-snapshot

Optimal IMF selection and unknown fault feature extraction for rolling bearings with different defect modes

inferredcites →https://doi.org/10.1016/j.measurement.2020.108185openalex-snapshot

An optimized VMD method and its applications in bearing fault diagnosis

inferredcites →https://doi.org/10.1016/j.neucom.2019.05.052openalex-snapshot

An improved deep convolutional neural network with multi-scale information for bearing fault diagnosis

inferredcites →https://doi.org/10.1109/access.2021.3059761openalex-snapshot

Rolling Bearing Fault Diagnosis Based on One-Dimensional Dilated Convolution Network With Residual Connection

inferredcites →https://doi.org/10.1109/jsen.2020.3030910openalex-snapshot

Fault Diagnosis of Rolling Bearings Based on an Improved Stack Autoencoder and Support Vector Machine

inferredcites →https://doi.org/10.1109/jsen.2020.3042182openalex-snapshot

Intelligent Rolling Bearing Fault Diagnosis via Vision ConvNet

inferredcites →https://doi.org/10.1109/tase.2021.3117288openalex-snapshot

Gear Fault Diagnosis Based on Variational Modal Decomposition and Wide+Narrow Visual Field Neural Networks

inferredcites →https://doi.org/10.1109/tie.2017.2762639openalex-snapshot

Deep Residual Networks With Dynamically Weighted Wavelet Coefficients for Fault Diagnosis of Planetary Gearboxes

inferredcites →https://doi.org/10.1109/tie.2017.2774777openalex-snapshot

A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method

inferredcites →https://doi.org/10.1109/tie.2018.2844805openalex-snapshot

Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox

inferredcites →https://doi.org/10.1109/tie.2018.2856205openalex-snapshot

The Optimized Deep Belief Networks With Improved Logistic Sigmoid Units and Their Application in Fault Diagnosis for Planetary Gearboxes of Wind Turbines

inferredcites →https://doi.org/10.1109/tie.2018.2866050openalex-snapshot

Multiple Wavelet Coefficients Fusion in Deep Residual Networks for Fault Diagnosis

inferredcites →https://doi.org/10.1109/tie.2019.2931255openalex-snapshot

Broad Convolutional Neural Network Based Industrial Process Fault Diagnosis With Incremental Learning Capability

inferredcites →https://doi.org/10.1109/tie.2020.2972458openalex-snapshot

Deep Residual Networks With Adaptively Parametric Rectifier Linear Units for Fault Diagnosis

inferredcites →https://doi.org/10.1109/tii.2019.2943898openalex-snapshot

Deep Residual Shrinkage Networks for Fault Diagnosis

inferredcites →https://doi.org/10.1109/tii.2020.2966326openalex-snapshot

One-Dimensional Residual Convolutional Autoencoder Based Feature Learning for Gearbox Fault Diagnosis

inferredcites →https://doi.org/10.1109/tii.2020.2967822openalex-snapshot

Gearbox Fault Diagnosis Using a Deep Learning Model With Limited Data Sample

inferredcites →https://doi.org/10.1109/tii.2020.3003353openalex-snapshot

Cascade Convolutional Neural Network With Progressive Optimization for Motor Fault Diagnosis Under Nonstationary Conditions

inferredcites →https://doi.org/10.1109/tim.2019.2890933openalex-snapshot

Novel Adaptive Search Method for Bearing Fault Frequency Using Stochastic Resonance Quantified by Amplitude-Domain Index

inferredcites →https://doi.org/10.1109/tim.2019.2925247openalex-snapshot

DCNN-Based Multi-Signal Induction Motor Fault Diagnosis

inferredcites →https://doi.org/10.1109/tim.2021.3077673openalex-snapshot

Multiscale Convolutional Neural Network With Feature Alignment for Bearing Fault Diagnosis

inferredcites →https://doi.org/10.1109/tim.2021.3112800openalex-snapshot

Deep Dynamic Adaptive Transfer Network for Rolling Bearing Fault Diagnosis With Considering Cross-Machine Instance

inferredcites →https://doi.org/10.1109/tim.2022.3196742openalex-snapshot

Multiscale Residual Attention Convolutional Neural Network for Bearing Fault Diagnosis

inferredcites →https://doi.org/10.1109/tim.2022.3200667openalex-snapshot

Domain Adaptive Remaining Useful Life Prediction With Transformer

inferredcites →https://doi.org/10.1109/tim.2022.3212525openalex-snapshot

Weighted Adversarial Domain Adaptation for Machine Remaining Useful Life Prediction

inferredcites →https://doi.org/10.3390/s17020425openalex-snapshot

A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Deep Learning Based Approaches for Intelligent Industrial Machineryhealth Management & Fault Diagnosis in Resource-Constrainedenvironments

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Interpretable Motor Sound Classification for Enhanced Fault Detection Leveraging Explainable AI

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Fault Diagnosis Method of the USV Based on Improved Res2Net Multiscale Feature Fusion

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A Generative Multiview Graph Contrastive Learning for Unsupervised Fault Diagnosis of Bogie Bearing

inferred← citeshttps://doi.org/10.1109/tim.2024.3366570openalex-snapshot

Deep Morphological Shrinkage Convolutional Autoencoder-Based Feature Learning of Vibration Signals for Gearbox Fault Diagnosis

inferred← citeshttps://doi.org/10.1109/tim.2024.3415783openalex-snapshot

Fault Diagnosis of Gearbox Based on Cross-Domain Transfer Learning With Fine-Tuning Mechanism Using Unbalanced Samples

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Damage Presence, Localization, and Quantification of Aircraft Structure Based on End-to-End Deep Learning Framework

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SRPKD-IGhostNet: A Highly Precise and Lightweight Fault Diagnosis Framework Based on Sample-Related Progressive Knowledge Distillation and Multiscale Fusion Convolution

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Research on fault tracing method of traction drive control system

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Advanced Fault Diagnosis in Rotating Machines Using 2D Grayscale Images with Improved Deep Convolutional Neural Networks

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Detection of Contamination and Failure in the Outer Race on Ceramic, Metallic, and Hybrid Bearings through AI Using Magnetic Flux and Current

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